Realizing the promise of precision medicine requires identifying and characterizing meaningful molecular and clinical heterogeneity in ways that are robust across studies, locations, and diverse populations.

I am an Assistant Professor in the Department of Medicine at Stanford (Division of Computational Medicine). My work spans computational medicine, computational biology, machine learning, and statistics. I develop new machine-learning methods and statistical models to uncover hidden signals in high-dimensional genomic and clinical datasets. A central goal of my research is to enable robust, replicable biomedical discoveries by addressing key challenges that limit the translation of data-driven findings into clinical impact, including hidden confounders, unrecognized disease subtypes, and constraints like data size or resolution. Among other examples, this research program has recently led to the identification of regulatory-like naïve T-cell populations associated with aging, a type 2 asthma sub-endotype linked to differential therapeutic response, and glaucoma patient subgroups with distinct outcomes under first-line treatments.

From 2022 to 2025, I was an Assistant Adjunct Professor of Computational Medicine at UCLA. Prior to that, I was a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, working with Nir Yosef and Michael Jordan. I earned my PhD in Computer Science at UCLA with Eran Halperin, and my MSc in Computer Science and BSc in Computer Science and Biology at Tel Aviv University.